Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations26637
Missing cells210
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 MiB
Average record size in memory216.0 B

Variable types

Numeric11
Categorical16

Alerts

MW is highly overall correlated with NumOfAtoms and 2 other fieldsHigh correlation
NumHBondDonors is highly overall correlated with hydroxyl (alkyl) and 1 other fieldsHigh correlation
NumOfAtoms is highly overall correlated with MW and 2 other fieldsHigh correlation
NumOfC is highly overall correlated with NumOfAtomsHigh correlation
NumOfConf is highly overall correlated with NumOfAtoms and 1 other fieldsHigh correlation
NumOfN is highly overall correlated with MW and 2 other fieldsHigh correlation
NumOfO is highly overall correlated with MW and 1 other fieldsHigh correlation
hydroxyl (alkyl) is highly overall correlated with NumHBondDonorsHigh correlation
log_pSat_Pa is highly overall correlated with NumHBondDonors and 1 other fieldsHigh correlation
nitrate is highly overall correlated with NumOfNHigh correlation
parentspecies is highly imbalanced (57.1%) Imbalance
C=C (non-aromatic) is highly imbalanced (72.2%) Imbalance
C=C-C=O in non-aromatic ring is highly imbalanced (93.9%) Imbalance
ester is highly imbalanced (59.2%) Imbalance
nitro is highly imbalanced (60.2%) Imbalance
aromatic hydroxyl is highly imbalanced (99.5%) Imbalance
carbonylperoxyacid is highly imbalanced (55.8%) Imbalance
nitroester is highly imbalanced (93.6%) Imbalance
ID is uniformly distributed Uniform
ID has unique values Unique
NumHBondDonors has 839 (3.1%) zeros Zeros
hydroxyl (alkyl) has 11258 (42.3%) zeros Zeros
ketone has 9962 (37.4%) zeros Zeros

Reproduction

Analysis started2024-11-20 15:04:51.483389
Analysis finished2024-11-20 15:05:09.208466
Duration17.73 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct26637
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15825.703
Minimum0
Maximum31636
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:09.315369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1600.8
Q17914
median15840
Q323720
95-th percentile30063.2
Maximum31636
Range31636
Interquartile range (IQR)15806

Descriptive statistics

Standard deviation9133.7084
Coefficient of variation (CV)0.57714393
Kurtosis-1.2008525
Mean15825.703
Median Absolute Deviation (MAD)7902
Skewness-0.00063423589
Sum4.2154925 × 108
Variance83424629
MonotonicityStrictly increasing
2024-11-20T17:05:09.482422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31636 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
Other values (26627) 26627
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
31636 1
< 0.1%
31635 1
< 0.1%
31634 1
< 0.1%
31633 1
< 0.1%
31632 1
< 0.1%
31631 1
< 0.1%
31630 1
< 0.1%
31629 1
< 0.1%
31628 1
< 0.1%
31627 1
< 0.1%

log_pSat_Pa
Real number (ℝ)

High correlation 

Distinct26591
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.5167469
Minimum-18.822563
Maximum8.3906421
Zeros0
Zeros (%)0.0%
Negative25710
Negative (%)96.5%
Memory size208.2 KiB
2024-11-20T17:05:09.639961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-18.822563
5-th percentile-10.793023
Q1-7.5151473
median-5.4505772
Q3-3.4291923
95-th percentile-0.54476426
Maximum8.3906421
Range27.213205
Interquartile range (IQR)4.085955

Descriptive statistics

Standard deviation3.1201914
Coefficient of variation (CV)-0.56558538
Kurtosis0.23876738
Mean-5.5167469
Median Absolute Deviation (MAD)2.0445682
Skewness-0.14345662
Sum-146949.59
Variance9.7355942
MonotonicityNot monotonic
2024-11-20T17:05:09.790165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08358874357 3
 
< 0.1%
0.01108456436 3
 
< 0.1%
0.3380478075 3
 
< 0.1%
0.9150408211 2
 
< 0.1%
0.474454863 2
 
< 0.1%
0.06874854832 2
 
< 0.1%
0.07758546079 2
 
< 0.1%
0.5585435055 2
 
< 0.1%
0.5107249221 2
 
< 0.1%
1.078352744 2
 
< 0.1%
Other values (26581) 26614
99.9%
ValueCountFrequency (%)
-18.82256317 1
< 0.1%
-18.7418241 1
< 0.1%
-18.56600316 1
< 0.1%
-18.42315725 1
< 0.1%
-18.33934555 1
< 0.1%
-18.26182172 1
< 0.1%
-18.11947865 1
< 0.1%
-17.77948235 1
< 0.1%
-17.74949752 1
< 0.1%
-17.37059213 1
< 0.1%
ValueCountFrequency (%)
8.39064211 1
< 0.1%
8.308679536 1
< 0.1%
6.927532486 1
< 0.1%
5.90998006 1
< 0.1%
5.740813427 1
< 0.1%
5.698798782 1
< 0.1%
5.621309793 1
< 0.1%
5.370839707 1
< 0.1%
5.138769508 1
< 0.1%
5.094720085 1
< 0.1%

MW
Real number (ℝ)

High correlation 

Distinct774
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.63834
Minimum30.010565
Maximum386.0445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:09.925387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum30.010565
5-th percentile179.0066
Q1233.01717
median266.98626
Q3299.01247
95-th percentile343.96117
Maximum386.0445
Range356.03394
Interquartile range (IQR)65.995309

Descriptive statistics

Standard deviation49.618151
Coefficient of variation (CV)0.18749419
Kurtosis-0.18936714
Mean264.63834
Median Absolute Deviation (MAD)32.985078
Skewness-0.21402159
Sum7049171.5
Variance2461.9609
MonotonicityNot monotonic
2024-11-20T17:05:10.071022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253.0069954 378
 
1.4%
267.0226455 312
 
1.2%
312.0077237 309
 
1.2%
265.0069954 307
 
1.2%
234.9964307 276
 
1.0%
237.0120808 270
 
1.0%
249.0120808 248
 
0.9%
269.00191 248
 
0.9%
283.0175601 247
 
0.9%
297.9920736 246
 
0.9%
Other values (764) 23796
89.3%
ValueCountFrequency (%)
30.01056468 1
< 0.1%
44.02621475 1
< 0.1%
60.02112937 1
< 0.1%
71.98474386 1
< 0.1%
72.02112937 1
< 0.1%
74.00039392 1
< 0.1%
74.03677943 2
< 0.1%
74.99564289 1
< 0.1%
76.01604399 1
< 0.1%
86.00039392 1
< 0.1%
ValueCountFrequency (%)
386.0445031 1
 
< 0.1%
386.0445031 35
 
0.1%
377.9666467 13
 
< 0.1%
377.9666467 2
 
< 0.1%
373.9717321 136
0.5%
370.0495885 25
 
0.1%
370.0495885 46
 
0.2%
368.0339384 37
 
0.1%
368.0339384 48
 
0.2%
361.9717321 74
0.3%

NumOfAtoms
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.251567
Minimum4
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:10.206811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile18
Q123
median26
Q330
95-th percentile36
Maximum41
Range37
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2298184
Coefficient of variation (CV)0.19921928
Kurtosis-0.15084496
Mean26.251567
Median Absolute Deviation (MAD)4
Skewness0.2106235
Sum699263
Variance27.351
MonotonicityNot monotonic
2024-11-20T17:05:10.340120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
25 2256
 
8.5%
27 2019
 
7.6%
24 1997
 
7.5%
26 1898
 
7.1%
22 1856
 
7.0%
23 1811
 
6.8%
28 1751
 
6.6%
29 1573
 
5.9%
21 1407
 
5.3%
30 1346
 
5.1%
Other values (28) 8723
32.7%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 5
 
< 0.1%
9 7
 
< 0.1%
10 8
 
< 0.1%
11 17
 
0.1%
12 25
0.1%
13 45
0.2%
ValueCountFrequency (%)
41 16
 
0.1%
40 95
 
0.4%
39 168
 
0.6%
38 213
 
0.8%
37 478
1.8%
36 518
1.9%
35 512
1.9%
34 747
2.8%
33 866
3.3%
32 772
2.9%

NumOfC
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8624094
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:10.458643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median7
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.453679
Coefficient of variation (CV)0.21183215
Kurtosis0.19243115
Mean6.8624094
Median Absolute Deviation (MAD)1
Skewness0.51077538
Sum182794
Variance2.1131826
MonotonicityNot monotonic
2024-11-20T17:05:10.555600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 9295
34.9%
6 8015
30.1%
5 2628
 
9.9%
10 2173
 
8.2%
9 2084
 
7.8%
8 1603
 
6.0%
4 694
 
2.6%
3 125
 
0.5%
2 18
 
0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 18
 
0.1%
3 125
 
0.5%
4 694
 
2.6%
5 2628
 
9.9%
6 8015
30.1%
7 9295
34.9%
8 1603
 
6.0%
9 2084
 
7.8%
10 2173
 
8.2%
ValueCountFrequency (%)
10 2173
 
8.2%
9 2084
 
7.8%
8 1603
 
6.0%
7 9295
34.9%
6 8015
30.1%
5 2628
 
9.9%
4 694
 
2.6%
3 125
 
0.5%
2 18
 
0.1%
1 2
 
< 0.1%

NumOfO
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9370425
Minimum0
Maximum17
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:10.652271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q18
median10
Q312
95-th percentile14
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4851671
Coefficient of variation (CV)0.25009123
Kurtosis-0.21760969
Mean9.9370425
Median Absolute Deviation (MAD)2
Skewness-0.092496213
Sum264693
Variance6.1760557
MonotonicityNot monotonic
2024-11-20T17:05:10.757970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
10 4073
15.3%
9 4033
15.1%
11 3709
13.9%
12 3160
11.9%
8 2941
11.0%
13 2435
9.1%
7 2222
8.3%
6 1307
 
4.9%
14 939
 
3.5%
15 619
 
2.3%
Other values (8) 1199
 
4.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 4
 
< 0.1%
2 33
 
0.1%
3 101
 
0.4%
4 226
 
0.8%
5 609
 
2.3%
6 1307
 
4.9%
7 2222
8.3%
8 2941
11.0%
9 4033
15.1%
ValueCountFrequency (%)
17 15
 
0.1%
16 210
 
0.8%
15 619
 
2.3%
14 939
 
3.5%
13 2435
9.1%
12 3160
11.9%
11 3709
13.9%
10 4073
15.3%
9 4033
15.1%
8 2941
11.0%

NumOfN
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
1
13074 
2
7628 
0
5935 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 13074
49.1%
2 7628
28.6%
0 5935
22.3%

Length

2024-11-20T17:05:10.874345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:10.967898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 13074
49.1%
2 7628
28.6%
0 5935
22.3%

Most occurring characters

ValueCountFrequency (%)
1 13074
49.1%
2 7628
28.6%
0 5935
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 13074
49.1%
2 7628
28.6%
0 5935
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 13074
49.1%
2 7628
28.6%
0 5935
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 13074
49.1%
2 7628
28.6%
0 5935
22.3%

NumHBondDonors
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2016368
Minimum0
Maximum6
Zeros839
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:11.064577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0210285
Coefficient of variation (CV)0.46375882
Kurtosis-0.13411816
Mean2.2016368
Median Absolute Deviation (MAD)1
Skewness0.22269893
Sum58645
Variance1.0424992
MonotonicityNot monotonic
2024-11-20T17:05:11.167534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 10219
38.4%
3 7106
26.7%
1 5790
21.7%
4 2332
 
8.8%
0 839
 
3.1%
5 335
 
1.3%
6 16
 
0.1%
ValueCountFrequency (%)
0 839
 
3.1%
1 5790
21.7%
2 10219
38.4%
3 7106
26.7%
4 2332
 
8.8%
5 335
 
1.3%
6 16
 
0.1%
ValueCountFrequency (%)
6 16
 
0.1%
5 335
 
1.3%
4 2332
 
8.8%
3 7106
26.7%
2 10219
38.4%
1 5790
21.7%
0 839
 
3.1%

NumOfConf
Real number (ℝ)

High correlation 

Distinct1056
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.85678
Minimum1
Maximum1743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:11.294714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q172
median173
Q3332
95-th percentile635
Maximum1743
Range1742
Interquartile range (IQR)260

Descriptive statistics

Standard deviation203.23431
Coefficient of variation (CV)0.88417802
Kurtosis2.4202296
Mean229.85678
Median Absolute Deviation (MAD)118
Skewness1.4087215
Sum6122695
Variance41304.185
MonotonicityNot monotonic
2024-11-20T17:05:11.440044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 126
 
0.5%
24 117
 
0.4%
31 117
 
0.4%
16 114
 
0.4%
42 114
 
0.4%
9 114
 
0.4%
43 112
 
0.4%
29 111
 
0.4%
15 107
 
0.4%
22 107
 
0.4%
Other values (1046) 25498
95.7%
ValueCountFrequency (%)
1 46
0.2%
2 52
0.2%
3 92
0.3%
4 74
0.3%
5 79
0.3%
6 88
0.3%
7 96
0.4%
8 87
0.3%
9 114
0.4%
10 103
0.4%
ValueCountFrequency (%)
1743 1
< 0.1%
1575 1
< 0.1%
1552 1
< 0.1%
1510 1
< 0.1%
1439 1
< 0.1%
1437 1
< 0.1%
1382 1
< 0.1%
1380 1
< 0.1%
1371 1
< 0.1%
1343 1
< 0.1%

NumOfConfUsed
Real number (ℝ)

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.700417
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:11.585631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median30
Q340
95-th percentile40
Maximum40
Range39
Interquartile range (IQR)29

Descriptive statistics

Standard deviation14.689993
Coefficient of variation (CV)0.57158578
Kurtosis-1.5052882
Mean25.700417
Median Absolute Deviation (MAD)10
Skewness-0.3726763
Sum684582
Variance215.79589
MonotonicityNot monotonic
2024-11-20T17:05:11.725726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
40 10825
40.6%
1 839
 
3.1%
2 743
 
2.8%
3 725
 
2.7%
4 675
 
2.5%
6 642
 
2.4%
5 628
 
2.4%
7 589
 
2.2%
8 565
 
2.1%
9 540
 
2.0%
Other values (30) 9866
37.0%
ValueCountFrequency (%)
1 839
3.1%
2 743
2.8%
3 725
2.7%
4 675
2.5%
5 628
2.4%
6 642
2.4%
7 589
2.2%
8 565
2.1%
9 540
2.0%
10 524
2.0%
ValueCountFrequency (%)
40 10825
40.6%
39 375
 
1.4%
38 247
 
0.9%
37 265
 
1.0%
36 239
 
0.9%
35 246
 
0.9%
34 223
 
0.8%
33 240
 
0.9%
32 246
 
0.9%
31 263
 
1.0%

parentspecies
Categorical

Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing210
Missing (%)0.8%
Memory size208.2 KiB
toluene
17950 
apin
6165 
decane
2218 
apin_decane
 
46
apin_toluene
 
37
Other values (2)
 
11

Length

Max length19
Median length7
Mean length6.2347977
Min length4

Characters and Unicode

Total characters164767
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtoluene
2nd rowapin
3rd rowapin
4th rowtoluene
5th rowtoluene

Common Values

ValueCountFrequency (%)
toluene 17950
67.4%
apin 6165
 
23.1%
decane 2218
 
8.3%
apin_decane 46
 
0.2%
apin_toluene 37
 
0.1%
apin_decane_toluene 9
 
< 0.1%
decane_toluene 2
 
< 0.1%
(Missing) 210
 
0.8%

Length

2024-11-20T17:05:11.871377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:11.994493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
toluene 17950
67.9%
apin 6165
 
23.3%
decane 2218
 
8.4%
apin_decane 46
 
0.2%
apin_toluene 37
 
0.1%
apin_decane_toluene 9
 
< 0.1%
decane_toluene 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 40546
24.6%
n 26530
16.1%
t 17998
10.9%
o 17998
10.9%
u 17998
10.9%
l 17998
10.9%
a 8532
 
5.2%
p 6257
 
3.8%
i 6257
 
3.8%
d 2275
 
1.4%
Other values (2) 2378
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 164767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 40546
24.6%
n 26530
16.1%
t 17998
10.9%
o 17998
10.9%
u 17998
10.9%
l 17998
10.9%
a 8532
 
5.2%
p 6257
 
3.8%
i 6257
 
3.8%
d 2275
 
1.4%
Other values (2) 2378
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 164767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 40546
24.6%
n 26530
16.1%
t 17998
10.9%
o 17998
10.9%
u 17998
10.9%
l 17998
10.9%
a 8532
 
5.2%
p 6257
 
3.8%
i 6257
 
3.8%
d 2275
 
1.4%
Other values (2) 2378
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 164767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 40546
24.6%
n 26530
16.1%
t 17998
10.9%
o 17998
10.9%
u 17998
10.9%
l 17998
10.9%
a 8532
 
5.2%
p 6257
 
3.8%
i 6257
 
3.8%
d 2275
 
1.4%
Other values (2) 2378
 
1.4%

C=C (non-aromatic)
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
24218 
1
 
2414
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24218
90.9%
1 2414
 
9.1%
2 5
 
< 0.1%

Length

2024-11-20T17:05:12.123443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:12.221390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24218
90.9%
1 2414
 
9.1%
2 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 24218
90.9%
1 2414
 
9.1%
2 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24218
90.9%
1 2414
 
9.1%
2 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24218
90.9%
1 2414
 
9.1%
2 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24218
90.9%
1 2414
 
9.1%
2 5
 
< 0.1%

C=C-C=O in non-aromatic ring
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
26329 
1
 
269
2
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26329
98.8%
1 269
 
1.0%
2 39
 
0.1%

Length

2024-11-20T17:05:12.320981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:12.410825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26329
98.8%
1 269
 
1.0%
2 39
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 26329
98.8%
1 269
 
1.0%
2 39
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26329
98.8%
1 269
 
1.0%
2 39
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26329
98.8%
1 269
 
1.0%
2 39
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26329
98.8%
1 269
 
1.0%
2 39
 
0.1%

hydroxyl (alkyl)
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82377895
Minimum0
Maximum5
Zeros11258
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:12.497659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86909396
Coefficient of variation (CV)1.0550087
Kurtosis0.55379325
Mean0.82377895
Median Absolute Deviation (MAD)1
Skewness0.94429217
Sum21943
Variance0.75532431
MonotonicityNot monotonic
2024-11-20T17:05:12.598251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11258
42.3%
1 10208
38.3%
2 3941
 
14.8%
3 1073
 
4.0%
4 151
 
0.6%
5 6
 
< 0.1%
ValueCountFrequency (%)
0 11258
42.3%
1 10208
38.3%
2 3941
 
14.8%
3 1073
 
4.0%
4 151
 
0.6%
5 6
 
< 0.1%
ValueCountFrequency (%)
5 6
 
< 0.1%
4 151
 
0.6%
3 1073
 
4.0%
2 3941
 
14.8%
1 10208
38.3%
0 11258
42.3%

aldehyde
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
14820 
1
9480 
2
2153 
3
 
181
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 14820
55.6%
1 9480
35.6%
2 2153
 
8.1%
3 181
 
0.7%
4 3
 
< 0.1%

Length

2024-11-20T17:05:12.707373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:12.806754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14820
55.6%
1 9480
35.6%
2 2153
 
8.1%
3 181
 
0.7%
4 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14820
55.6%
1 9480
35.6%
2 2153
 
8.1%
3 181
 
0.7%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14820
55.6%
1 9480
35.6%
2 2153
 
8.1%
3 181
 
0.7%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14820
55.6%
1 9480
35.6%
2 2153
 
8.1%
3 181
 
0.7%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14820
55.6%
1 9480
35.6%
2 2153
 
8.1%
3 181
 
0.7%
4 3
 
< 0.1%

ketone
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93013477
Minimum0
Maximum5
Zeros9962
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size208.2 KiB
2024-11-20T17:05:12.903685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89732254
Coefficient of variation (CV)0.96472314
Kurtosis0.07079395
Mean0.93013477
Median Absolute Deviation (MAD)1
Skewness0.74504856
Sum24776
Variance0.80518775
MonotonicityNot monotonic
2024-11-20T17:05:13.004106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10142
38.1%
0 9962
37.4%
2 5151
19.3%
3 1199
 
4.5%
4 180
 
0.7%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 9962
37.4%
1 10142
38.1%
2 5151
19.3%
3 1199
 
4.5%
4 180
 
0.7%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 180
 
0.7%
3 1199
 
4.5%
2 5151
19.3%
1 10142
38.1%
0 9962
37.4%

carboxylic acid
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
18141 
1
7797 
2
 
685
3
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18141
68.1%
1 7797
29.3%
2 685
 
2.6%
3 14
 
0.1%

Length

2024-11-20T17:05:13.110631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:13.206261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18141
68.1%
1 7797
29.3%
2 685
 
2.6%
3 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 18141
68.1%
1 7797
29.3%
2 685
 
2.6%
3 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18141
68.1%
1 7797
29.3%
2 685
 
2.6%
3 14
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18141
68.1%
1 7797
29.3%
2 685
 
2.6%
3 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18141
68.1%
1 7797
29.3%
2 685
 
2.6%
3 14
 
0.1%

ester
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
23292 
1
2519 
2
 
826

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23292
87.4%
1 2519
 
9.5%
2 826
 
3.1%

Length

2024-11-20T17:05:13.316067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:13.412466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23292
87.4%
1 2519
 
9.5%
2 826
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 23292
87.4%
1 2519
 
9.5%
2 826
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23292
87.4%
1 2519
 
9.5%
2 826
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23292
87.4%
1 2519
 
9.5%
2 826
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23292
87.4%
1 2519
 
9.5%
2 826
 
3.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
21152 
1
5485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 21152
79.4%
1 5485
 
20.6%

Length

2024-11-20T17:05:13.518721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:13.612128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21152
79.4%
1 5485
 
20.6%

Most occurring characters

ValueCountFrequency (%)
0 21152
79.4%
1 5485
 
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21152
79.4%
1 5485
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21152
79.4%
1 5485
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21152
79.4%
1 5485
 
20.6%

nitrate
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
11948 
1
11593 
2
3096 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 11948
44.9%
1 11593
43.5%
2 3096
 
11.6%

Length

2024-11-20T17:05:13.711311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:13.807245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11948
44.9%
1 11593
43.5%
2 3096
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 11948
44.9%
1 11593
43.5%
2 3096
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11948
44.9%
1 11593
43.5%
2 3096
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11948
44.9%
1 11593
43.5%
2 3096
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11948
44.9%
1 11593
43.5%
2 3096
 
11.6%

nitro
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
22588 
1
3995 
2
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22588
84.8%
1 3995
 
15.0%
2 54
 
0.2%

Length

2024-11-20T17:05:13.909834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:14.005756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22588
84.8%
1 3995
 
15.0%
2 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 22588
84.8%
1 3995
 
15.0%
2 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22588
84.8%
1 3995
 
15.0%
2 54
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22588
84.8%
1 3995
 
15.0%
2 54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22588
84.8%
1 3995
 
15.0%
2 54
 
0.2%

aromatic hydroxyl
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
26619 
1
 
10
2
 
5
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26619
99.9%
1 10
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%

Length

2024-11-20T17:05:14.108970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:14.203666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26619
99.9%
1 10
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 26619
99.9%
1 10
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26619
99.9%
1 10
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26619
99.9%
1 10
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26619
99.9%
1 10
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
20475 
1
5882 
2
 
280

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20475
76.9%
1 5882
 
22.1%
2 280
 
1.1%

Length

2024-11-20T17:05:14.308259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:14.402771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20475
76.9%
1 5882
 
22.1%
2 280
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 20475
76.9%
1 5882
 
22.1%
2 280
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20475
76.9%
1 5882
 
22.1%
2 280
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20475
76.9%
1 5882
 
22.1%
2 280
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20475
76.9%
1 5882
 
22.1%
2 280
 
1.1%

peroxide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
19147 
1
7490 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19147
71.9%
1 7490
 
28.1%

Length

2024-11-20T17:05:14.506679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:14.597925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19147
71.9%
1 7490
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 19147
71.9%
1 7490
 
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19147
71.9%
1 7490
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19147
71.9%
1 7490
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19147
71.9%
1 7490
 
28.1%

hydroperoxide
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
1
12913 
0
10028 
2
3492 
3
 
203
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 12913
48.5%
0 10028
37.6%
2 3492
 
13.1%
3 203
 
0.8%
4 1
 
< 0.1%

Length

2024-11-20T17:05:14.695369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:15.022032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 12913
48.5%
0 10028
37.6%
2 3492
 
13.1%
3 203
 
0.8%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 12913
48.5%
0 10028
37.6%
2 3492
 
13.1%
3 203
 
0.8%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12913
48.5%
0 10028
37.6%
2 3492
 
13.1%
3 203
 
0.8%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12913
48.5%
0 10028
37.6%
2 3492
 
13.1%
3 203
 
0.8%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12913
48.5%
0 10028
37.6%
2 3492
 
13.1%
3 203
 
0.8%
4 1
 
< 0.1%

carbonylperoxyacid
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
20039 
1
6247 
2
 
346
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 20039
75.2%
1 6247
 
23.5%
2 346
 
1.3%
3 5
 
< 0.1%

Length

2024-11-20T17:05:15.127514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:15.222117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20039
75.2%
1 6247
 
23.5%
2 346
 
1.3%
3 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 20039
75.2%
1 6247
 
23.5%
2 346
 
1.3%
3 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20039
75.2%
1 6247
 
23.5%
2 346
 
1.3%
3 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20039
75.2%
1 6247
 
23.5%
2 346
 
1.3%
3 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20039
75.2%
1 6247
 
23.5%
2 346
 
1.3%
3 5
 
< 0.1%

nitroester
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size208.2 KiB
0
26294 
1
 
338
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26637
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26294
98.7%
1 338
 
1.3%
2 5
 
< 0.1%

Length

2024-11-20T17:05:15.324271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T17:05:15.426912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26294
98.7%
1 338
 
1.3%
2 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 26294
98.7%
1 338
 
1.3%
2 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26294
98.7%
1 338
 
1.3%
2 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26294
98.7%
1 338
 
1.3%
2 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26637
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26294
98.7%
1 338
 
1.3%
2 5
 
< 0.1%

Interactions

2024-11-20T17:05:07.311852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:54.624399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.649393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.760895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.898261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.226002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.386685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.587591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.714571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.867225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.185215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:07.411391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:55.171430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.752569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.865186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.002657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.322053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.494501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.688256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.819198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.007216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.287394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:07.508496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:55.673238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.848528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.966708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.107415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.417968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.597235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.786829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.922476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.125464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.389194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:07.608496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:55.783548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.952657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.071643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.219790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.526477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.707854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.899018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.029647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.246696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.494338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:07.712164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:55.904517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.058935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.180773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.325180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.632907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.822128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.003983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.137445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.367738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.601523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:08.022482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.008296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.151537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.275456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.420220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.727124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.933246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.100197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.232558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.474985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.695021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:08.120494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.124703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.257696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.381962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.695272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.830789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.047536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.206882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.337373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.605525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.802446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:08.216611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.229347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.357905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.483385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.795410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.957341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.156794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.309462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.440376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.720941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.905456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:08.316400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.340523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.462627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.590024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:59.909709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.074824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.267287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.414037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.545088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.843341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:07.010943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:08.421169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.453301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.568233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.699749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.020414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.195619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.383323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.523973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.655946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:05.967006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:07.118347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:08.511462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:56.552714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:57.664257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:04:58.800132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:00.123619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:01.293115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:02.485025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:03.617778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:04.753942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:06.077569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T17:05:07.215817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-20T17:05:15.543053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
C=C (non-aromatic)C=C-C=O in non-aromatic ringIDMWNumHBondDonorsNumOfAtomsNumOfCNumOfConfNumOfConfUsedNumOfNNumOfOaldehydearomatic hydroxylcarbonylperoxyacidcarbonylperoxynitratecarboxylic acidesterether (alicyclic)hydroperoxidehydroxyl (alkyl)ketonelog_pSat_Panitratenitronitroesterparentspeciesperoxide
C=C (non-aromatic)1.0000.2510.0000.1130.0320.1620.1410.1380.0910.0920.0900.0280.0000.0380.0000.0420.0750.1510.0220.0680.1460.0410.1350.0570.0090.1330.034
C=C-C=O in non-aromatic ring0.2511.0000.0120.0620.0080.0750.0690.0670.0760.0560.0520.0530.0000.0170.0240.0000.0250.0150.0030.0310.0620.0140.0370.0000.0010.0510.117
ID0.0000.0121.0000.0020.0040.0040.000-0.003-0.0010.0090.0000.0000.0000.0000.0080.0070.0000.0190.0080.007-0.0010.0030.0000.0140.0000.0060.000
MW0.1130.0620.0021.0000.0610.7050.3380.4530.2830.6420.8800.0550.0620.0570.3580.0460.0320.0920.0690.029-0.026-0.1660.3630.0810.0240.2590.127
NumHBondDonors0.0320.0080.0040.0611.000-0.002-0.1530.458-0.2780.2040.2270.0670.0080.1180.0850.1640.0550.1400.2000.597-0.316-0.6790.2050.0760.0120.1330.116
NumOfAtoms0.1620.0750.0040.705-0.0021.0000.7890.5160.3980.3740.3360.0930.0490.0490.1500.1020.1520.2550.100-0.0240.040-0.2940.3570.1410.0430.3200.231
NumOfC0.1410.0690.0000.338-0.1530.7891.0000.2710.2810.107-0.0640.0800.0000.0550.0650.0890.1600.2720.045-0.1010.266-0.2700.1990.1670.0400.3690.297
NumOfConf0.1380.067-0.0030.4530.4580.5160.2711.0000.4440.0930.3800.0330.0000.0490.0570.1480.0800.1420.1880.157-0.056-0.5480.1050.0780.0000.0830.155
NumOfConfUsed0.0910.076-0.0010.283-0.2780.3980.2810.4441.0000.2010.1260.0180.0380.1520.1150.0320.0600.1810.112-0.3410.1010.0290.2040.1290.0330.1590.161
NumOfN0.0920.0560.0090.6420.2040.3740.1070.0930.2011.0000.5220.1000.0130.0600.2800.0900.0710.0330.1360.0470.0670.1610.5690.1670.0430.0820.016
NumOfO0.0900.0520.0000.8800.2270.336-0.0640.3800.1260.5221.0000.0350.0730.1040.4130.0440.0620.0500.0610.108-0.115-0.1230.2260.1110.0330.2050.277
aldehyde0.0280.0530.0000.0550.0670.0930.0800.0330.0180.1000.0351.0000.0000.0660.0670.0630.0220.0850.0230.0230.1030.0260.0600.0300.0160.1110.072
aromatic hydroxyl0.0000.0000.0000.0620.0080.0490.0000.0000.0380.0130.0730.0001.0000.0000.0000.0000.0000.0080.0000.0110.0140.0000.0170.0160.0000.0000.012
carbonylperoxyacid0.0380.0170.0000.0570.1180.0490.0550.0490.1520.0600.1040.0660.0001.0000.0940.1020.0130.1100.1100.0440.0790.0660.0150.0100.0000.0770.027
carbonylperoxynitrate0.0000.0240.0080.3580.0850.1500.0650.0570.1150.2800.4130.0670.0000.0941.0000.0760.0000.0510.0440.0360.0920.1610.1640.0800.0240.0790.000
carboxylic acid0.0420.0000.0070.0460.1640.1020.0890.1480.0320.0900.0440.0630.0000.1020.0761.0000.0330.0890.0800.0500.0990.1780.0590.0290.0000.1110.036
ester0.0750.0250.0000.0320.0550.1520.1600.0800.0600.0710.0620.0220.0000.0130.0000.0331.0000.1930.0950.0280.0930.0660.0780.0620.1200.1840.237
ether (alicyclic)0.1510.0150.0190.0920.1400.2550.2720.1420.1810.0330.0500.0850.0080.1100.0510.0890.1931.0000.0330.0420.2520.0510.1340.1260.0580.3500.318
hydroperoxide0.0220.0030.0080.0690.2000.1000.0450.1880.1120.1360.0610.0230.0000.1100.0440.0800.0950.0331.0000.1050.0520.1590.1000.0260.0130.0560.116
hydroxyl (alkyl)0.0680.0310.0070.0290.597-0.024-0.1010.157-0.3410.0470.1080.0230.0110.0440.0360.0500.0280.0420.1051.000-0.114-0.2890.1160.0920.0170.1190.208
ketone0.1460.062-0.001-0.026-0.3160.0400.266-0.0560.1010.067-0.1150.1030.0140.0790.0920.0990.0930.2520.052-0.1141.0000.1560.0400.0330.0250.1040.092
log_pSat_Pa0.0410.0140.003-0.166-0.679-0.294-0.270-0.5480.0290.161-0.1230.0260.0000.0660.1610.1780.0660.0510.159-0.2890.1561.0000.1120.0340.0360.1290.041
nitrate0.1350.0370.0000.3630.2050.3570.1990.1050.2040.5690.2260.0600.0170.0150.1640.0590.0780.1340.1000.1160.0400.1121.0000.1850.0510.2210.058
nitro0.0570.0000.0140.0810.0760.1410.1670.0780.1290.1670.1110.0300.0160.0100.0800.0290.0620.1260.0260.0920.0330.0340.1851.0000.2090.2040.116
nitroester0.0090.0010.0000.0240.0120.0430.0400.0000.0330.0430.0330.0160.0000.0000.0240.0000.1200.0580.0130.0170.0250.0360.0510.2091.0000.0530.071
parentspecies0.1330.0510.0060.2590.1330.3200.3690.0830.1590.0820.2050.1110.0000.0770.0790.1110.1840.3500.0560.1190.1040.1290.2210.2040.0531.0000.364
peroxide0.0340.1170.0000.1270.1160.2310.2970.1550.1610.0160.2770.0720.0120.0270.0000.0360.2370.3180.1160.2080.0920.0410.0580.1160.0710.3641.000

Missing values

2024-11-20T17:05:08.669763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-20T17:05:09.032232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDlog_pSat_PaMWNumOfAtomsNumOfCNumOfONumOfNNumHBondDonorsNumOfConfNumOfConfUsedparentspeciesC=C (non-aromatic)C=C-C=O in non-aromatic ringhydroxyl (alkyl)aldehydeketonecarboxylic acidesterether (alicyclic)nitratenitroaromatic hydroxylcarbonylperoxynitrateperoxidehydroperoxidecarbonylperoxyacidnitroester
00-11.295070224.016832236904485.040.0toluene0011010100000200
11-4.782500310.0648453591021236.040.0apin0001100020000100
22-6.204319368.03393837101321308.040.0apin0001200010010100
33-9.672591299.0124752971214769.03.0toluene0020200010000110
44-4.252058202.01135320770177.032.0toluene0002200100000100
55-9.843756238.032482267903483.040.0NaN0011100000001200
661.936301241.965859183112141.017.0toluene0011000001011000
77-10.476294303.0073892961315238.029.0toluene0020000010001300
88-5.617627284.9968252661213487.040.0toluene0011000100010200
99-6.581878310.9760892671312378.040.0toluene0001102010000110
IDlog_pSat_PaMWNumOfAtomsNumOfCNumOfONumOfNNumHBondDonorsNumOfConfNumOfConfUsedparentspeciesC=C (non-aromatic)C=C-C=O in non-aromatic ringhydroxyl (alkyl)aldehydeketonecarboxylic acidesterether (alicyclic)nitratenitroaromatic hydroxylcarbonylperoxynitrateperoxidehydroperoxidecarbonylperoxyacidnitroester
2662731627-3.608671357.9768173071522330.027.0toluene0021000000021000
2662831628-12.154078315.0073893071314554.07.0toluene0011000110000210
2662931629-4.189750239.027731256912272.040.0apin0001100010000200
2663031630-7.013726266.9862602361113346.011.0toluene1000011001000110
2663131631-7.552853265.006995257101280.014.0toluene0000100101000020
2663231632-1.210727221.01716622681147.037.0toluene0001100110000100
2663331633-7.525230222.001182216903323.012.0toluene0012020000001000
2663431634-8.852094287.0124752861214362.011.0toluene0021000010001200
2663531635-6.564478284.9968252661213322.035.0toluene0010100010001110
2663631636-2.796255267.0226452771012144.023.0apin0010100000010010